The quantitative results obtained on the MIT300 [1]}, MIT1003 [2]}, TORONTO [3]}, PASCAL-S [4]} and DUT-OMRON [5]} datasets
are presented in Table REF , Table REF , Table REF , Table REF  and Table REF , respectively. In MIT300 dataset (see Table REF ), our model is struggle to compete with DeeFix [6]} and SALICON [7]} models. DeepFix introduces more complex network architecture and considers center prior. Besides, it is trained with more samples (SALICON dataset + 2700 extra images with actual eye fixation data). SALICON model is fine-tuned with more complex objective functions and images from OSIE dataset [8]}. Our method achieves promising results across a wide of datasets, which verifies its robustness and generality. With a relatively lightweight architecture, our work is meaningful for inspiring future work in this direction and offers a deep insight into the advantage of multiscale saliency cues for visual attention prediction.
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